Lung Cancer Screening Before and After a Multifaceted Electronic Health Record Intervention

This nonrandomized controlled trial investigates the association of an electronic health record–based lung cancer screening intervention with improved screening-related care.


Introduction
Lung cancer, the leading cause of cancer-related deaths in the US, continues to be a formidable health challenge. 1The US Preventive Services Task Force (USPSTF) recommends offering lung cancer screening (LCS) through low-dose computed tomography (LDCT) to individuals with a significant tobacco use history. 2,3Although LCS reduces mortality by as much as 20%, 4,5 the LCS rate among eligible individuals was approximately 6.5% in 2020. 6 address low LCS rates, health care systems across the US are beginning to implement clinical decision support (CDS) interventions to support LCS. [7][8][9][10] In a study at University of Utah Health (UUH), a multifaceted intervention, including clinician-facing reminders and an electronic health record (EHR)-integrated shared decision-making (SDM) tool, was associated with an increase in LDCT ordering (adjusted odds ratio [OR], 4.9, 95% CI, 3.4-6.9). 7,8Another study reported that implementing EHR prompts was associated with an increase in LDCT ordering (adjusted OR, 1.04; 95% CI, 1.01-1.07). 9ese prior studies of EHR-integrated CDS interventions focused on LCS ordering and completion among patients who had not undergone LCS in the past year. 8,9While LDCT is central to meeting patient needs related to LCS, it is not the only acceptable approach for closing this care gap.
Specifically, patients can be screened for lung cancer through other chest CTs (eg, diagnostic chest CTs) conducted for purposes other than LCS.Furthermore, patients may decline screening after SDM given that there are substantial potential downsides to screening and wide individual variation in patient lung cancer risk, life expectancy, and potential net benefit from screening. 11Therefore, other chest CTs or LCS SDM should be considered in addition to LDCT as valid approaches to LCS care gap closure.This study's primary goal was to provide a more holistic understanding of the association of a multifaceted, EHR-based CDS intervention with LCS care gap closure by accounting for these various approaches.A secondary goal was to evaluate whether providing simple patient portal reminders for LCS-eligible patients was associated with additional improvements in care cap closure.The primary study objective was to evaluate changes in LCS care gap closure via any means (ie, LDCT, other chest CT, or SDM) after the introduction of a multifaceted, EHR-integrated clinician-facing intervention (period 1) and the addition of patient-facing EHR patient portal reminders (period 2).
The study was approved by the University of Utah Institutional Review Board and registered with ClinicalTrials.gov(NCT04498052; see trial protocol in Supplement 1).There were no significant deviations from the registered trial.A waiver of consent was approved by the University of Utah Institutional Review Board because the intervention, which follows USPSTF guidelines, does not add more risk than the current standard of care and measures were in place to minimize privacy risks.Data were obtained from the UUH Enterprise Data Warehouse on September 14, 2023.This report follows the Transparent Reporting of Evaluations With Nonrandomized Designs (TREND) reporting guideline.

Setting
The research was carried out across 28 primary care and 4 pulmonary clinics located at 12 UUH locations.UUH uses a decentralized approach to LCS whereby frontline clinicians, such as primary care clinicians and pulmonologists, refer eligible and interested patients for LCS.The UUH LCS program is accredited by the American College of Radiology, and the Huntsman Cancer Institute maintains a registry of patients who have undergone LCS.There were 2 clinics at 1 location that started the intervention but were excluded because they closed before study completion.
UUH uses the Epic EHR system (versions February 2019, August 2019, February 2020, August 2020, February 2021, and May 2022).The creation of the multifaceted LCS CDS intervention was led by a collaborative initiative known as ReImagine EHR, 12 which applies interoperable EHR innovations to patient care.

Participants
Study eligibility criteria were evaluated at the level of primary care visits (office visits or telehealth visits) during the study period.Patients were eligible if they met 2013 USPSTF LCS eligibility criteria at the time of the visit and had at least 1 primary care visit in the preceding year.Per USPSTF guidelines, a person qualified for LCS if they were aged 55 to 80 years, had a smoking history of 30 pack-years or greater, actively smoked or had quit in the previous 15 years, and had not been diagnosed with lung cancer. 2While USPSTF expanded these criteria in 2021, 13 this analysis used 2013 criteria to maintain comparable patient populations across study periods.Inclusion and exclusion criteria were determined using EHR data on smoking, demographics, problem list entries, medical history, and encounter diagnoses.

Interventions
Period 1 CDS tools included clinician-facing reminders for LCS and LCS discussion in the EHR system Health Maintenance module, an EHR-integrated SDM tool (Figure 1), and narrative guidance provided in the LDCT ordering screen regarding LCS guidelines, including requirements from the Centers for Medicare & Medicaid Services to conduct SDM using a decision aid prior to initiating screening. 11The period 1 intervention was described previously 8 and is detailed in the eMethods in Supplement 2.
In period 2, patient-facing reminders were added.These reminders were part of the EHR system Health Maintenance module, in which care gaps can be optionally presented to patients on the main portal screen.Reminders can also be accessed through the patient portal menu.Patient reminders consisted of notifications on the need for Lung Cancer Screening or Lung Cancer Screening Discussion based on whether patients required screening or an initial discussion on screening.No further information (eg, an explanation of LCS) was provided due to patient portal limitations.
In period 1, the SDM tool and clinician reminders supported 2013 USPSTF guidelines. 2 Individualized predictions of net benefit in the SDM tool were based on the Bach risk model. 14,15In period 2, the SDM tool and clinician reminders were updated to support 2021 USPSTF guidelines. 13e SDM tool was updated to use the life-years gained from screening (LYFS)-CT model at this time to account for the higher risk of lung cancer among Black individuals; a threshold of at least 16.2 days of life expected to be gained from screening was used to identify patients expected to have a high benefit. 16Study interventions were communicated to users via typical channels for notifying clinicians regarding EHR system updates, including EHR-integrated prompts described previously and inclusion in brief updates on new EHR features presented at clinician meetings.

Primary Outcomes
The primary outcome was LCS care gap closure through any means.Care gap closure through any means could be achieved through LDCT completion in the past year, completion of another chest CT in the past year, or SDM documentation in the past 3 years.To assess population care gap closure levels at the end of each study period, we estimated the care gap closure status for all patients who had primary care visits in the 12 months preceding the last day of the period.Using structured EHR data, we considered SDM documented if a clinician noted that the need for LCS discussion was addressed, the patient declined screening, or LCS was not appropriate. 8The primary hypothesis was that introduction of the multifaceted, EHR-integrated intervention would be associated with increased LCS care gap closure.

Secondary Outcomes
Secondary outcomes included component mechanisms for achieving care gap closure (ie, through LDCTs, other chest CTs, and SDM), 3 mechanisms for unclosed care gaps (LCS ordered in past year but not completed, LCS elected in past 3 years but not ordered, and LCS not ordered and SDM not completed), and SDM tool use in the past 3 years.Of care gap closure mechanisms, closure through LDCTs was of particular interest.

Covariates
To ensure that the patient population was stable across study periods, we assessed patient characteristics, including estimated screening benefit level, 16 receipt of care in pulmonary clinics, sex, race and ethnicity, age, tobacco use, comorbidities, body mass index (calculated as weight in kilograms divided by height in meters squared), insurance status, family history of lung cancer, and patient portal use in the past year.The screening benefit level was reported in accordance with guidance from the American College of Chest Physicians to identify patients who might obtain the most benefit from screening (patients expected to have a high benefit). 17To identify these patients, we used LYFS-CT models, with a benchmark of an anticipated gain of at least 16.2 days of life. 16We identified 13 medical conditions or comorbidities from the problem list, medical history, and visit diagnoses.Race and ethnicity were derived from the EHR, where they were documented based on patient self-report as a part of routine clinical care.Race was documented as American Indian and Alaska Native, Asian, Black or African American, Native Hawaiian and Other Pacific Islander, White or Caucasian, and choose not to disclose.Ethnicity was documented as Hispanic or Latino, not Hispanic or Latino, and choose not to disclose.We aggregated race and ethnicity data into Hispanic or Latino, Black or African American, White or Caucasian, and other.The other race and ethnicity category included individuals of non-Hispanic ethnicity who selected a race of American Indian and Alaska Native, Asian, Native Hawaiian and Pacific Islander, or choose not to disclose.Non-Hispanic individuals were also categorized as other race and ethnicity if they selected more than 1 race or had no race selection in the system.Additionally, we reported Utah COVID-19 hospitalization rates as the 7-day mean of hospitalizations during the patient's last eligible visit during each period. 18Patient characteristics were calculated as of the last date of each study period, with the last known observation carried forward for categorical variables.For overall patient demographics, we used the last date of the baseline period.

Statistical Analysis
We used median and IQR to summarize continuous characteristics and count and percentage to summarize categorical characteristics.To compare characteristics across study periods, we used generalized linear models.To compare period 1 with baseline and period 2 with baseline, indicators of whether patients had visits in both periods were added to the model.

Primary Analysis: ITS
To evaluate the association of interventions with care gap closure rates, we conducted ITS analysis using the segmented regression approach.We chose segmented regression analysis because it allowed us to assess preexisting trends and changes in the slope and level of outcomes. 19Each patient was assigned a value of 1 (for care gap closure) or 0 (for care gap nonclosure) at the end of each month.The mean of these values for each month was found, and values were fitted into segmented linear regression models with study periods constituting 3 segments.We used the segmented least squares approach with parameters for intercept, baseline trend, and changes in the level and trend after the intervention.Care gap closure rates were stratified by estimated screening benefit level.We expected that implementing the tool would be associated with a higher rate of care gap closure in study period 1 compared with baseline, period 2 compared with period 1, and among patients with a high benefit compared with patients who were eligible but with intermediate benefit.

Secondary Analysis: Covariate Balancing Propensity Score
For primary and secondary outcome measures, we also estimated adjusted intervention outcomes after controlling for all characteristics using the covariate balancing propensity score (CBPS) approach. 20For binary outcomes, we used logistic regression.Improvements in covariate balance after propensity score adjustment are summarized in eFigure 2 in Supplement 2.
Statistical significance was defined at alpha = .05.We used R statistical software version 4.

Patient Characteristics
There were 19 008 patients who met inclusion criteria (eTable 1 in Supplement 2).Using exclusion criteria consecutively, we excluded 7698 patients (40.5%) due to insufficient detailed smoking data in the EHR to determine eligibility, 10 847 patients (57.1%) due to not meeting USPSTF criteria for lung cancer screening based on detailed smoking data, and 75 patients (0.4%) due to lung cancer  1).The COVID-19 pandemic started during period 1.
The pandemic was associated with increased use of the patient portal and telehealth visits.percentage points) and 2.4 percentage points (95% CI, 0.9 to 3.9 percentage points), respectively, and their slopes decreased by −1.7 percentage points (95% CI, −1.9 to −1.5 percentage points) per month and −1.2 percentage points (95% CI, −1.4 to −0.9 percentage points) per month, respectively.
Analyses stratified by patient benefit level showed similar trends as the overall analysis.The increase in the slope of LDCT completion in period 1 was higher for patients with a high benefit compared with those with an intermediate benefit (1.9 percentage points; 95% CI, 1.6 to 2.1 percentage points per month vs 1.1 percentage points; 95% CI, 0.9 to 1.4 percentage points per month).

Discussion
This nonrandomized controlled trial found that introduction of a multifaceted, EHR-integrated intervention was associated with improved LCS care gap closure in an academic health care system.
Introduction of clinician-facing interventions was associated with improvement (period 1), with a slight further increase associated with the addition of patient-facing reminders (period 2).Although the care gap closure rate slowed in period 2, eligible patients continued to have their care gaps closed throughout the study.While both clinician-and patient-facing LCS interventions showed potential benefits, 43.6% of screening-eligible patients still lacked LDCT orders or discussions at the end of .005−1.0 (−2.4 to 0.4) .17 <.001 2.4 (0.9 to 3.9) .002 −1.    a Estimates are at the end of each period.
b All comparisons are vs the baseline period.
c Adjusted ORs were calculated based on the propensity score approach.

Limitations
This study has several limitations.First, we used a nonrandomized ITS study design without parallel controls.The onset of the COVID-19 pandemic during the baseline period posed potential biases, such as reduced LCS rates due to diminished clinician capacity.We believe that the small increase in other chest CTs may be attributed to the COVID-19 pandemic rather than to our intervention.
Furthermore, USPSTF guideline changes in 2021 resulted in a larger number of patients becoming eligible for LCS.We used more stringent 2013 USPSTF criteria to maintain compatibility with period 1, but we were unable to fully separate the intervention outcome from potential outcomes associated with the guideline change itself.To mitigate the impact of this limitation, we used propensity score analysis techniques to account for differences in covariates.Second, our study took place in a single academic health care system, limiting generalizability.Third, we depended on EHR smoking history data, which may underestimate patient eligibility due to inaccuracies. 23Fourth, the quality of SDM was not evaluated.Fifth, due to the limited quality of EHR smoking data, a large proportion of individuals had insufficient detailed smoking history data to determine their eligibility.We are currently implementing follow-up interventions aimed at improving the identification of patients eligible for LCS.

Conclusions
In this nonrandomized controlled trial, implementing a comprehensive intervention that combined clinician-facing EHR reminders, an EHR-integrated SDM tool for personalized screening, narrative guidance presented in the LDCT order screen, and patient-facing reminders was associated with an increase in LCS care gap closure.Further research is needed to improve LCS, including through improved documentation of detailed smoking history in the EHR, improved LDCT follow-through rates, and more effective engagement of patients in their care.

Figure
Figure 2. Changes in Primary Outcomes associated with offering more robust patient-facing interventions, including individualized patient education through the patient portal.

Table 1 .
Patient Characteristics a P values are based on generalized linear models and are all vs baseline.bTheother race and ethnicity group consisted of individuals of non-Hispanic ethnicity with the following race responses: American Indian and Alaska Native, Asian, Native Hawaiian and Pacific Islander, more than 1 race, and chose not to disclose.

Table 3
with adjusted ORs estimated via CBPS.This analysis confirmed results for the ITS analysis.The intervention was associated with an increase in LCS care gap closure, from 175 of 1104 patients (15.9%) at the end of the baseline period to 510 of 1219 patients (41.8%) at the end of intervention period 1 (adjusted OR vs baseline, 3.7; 95% CI, 3.04.6)and588 of 1255 patients (46.9%) at the end of intervention period 2 (adjusted OR vs baseline, 4.3; 95% CI, 3.5,-5.3).At the end of intervention period 2, most patients (298 patients[23.7%])achievedcaregapclosurethough LDCT, followed by documentation of SDM in Health Maintenance modules (159 patients [12.7%]) and other chest CT (131 patients[10.4%]).For 107 patients in period 2 [8.5%],LCS was ordered but not completed.The number of patients for whom neither SDM nor LCS were completed decreased from 889 patients (80.5%) at baseline to 547 patients (43.6%) at the end of intervention period 2 (adjusted OR vs baseline, 0.2; 95% CI, 0.2-0.2).The SDM tool was used for 168 of 1255 eligible patients (13.4%) in period 2.Most of the increase in care-gap closure was contributed by increases in LDCT and LCS SDM.

Table 2 .
ITS Analysis: Association of Intervention With Care Gap Closure [23][24][25][26]imary OutcomesLung cancer screening care gap closure through any means and low-dose computed tomography (LDCT)-based gap closure is presented overall (A) and by patient benefit level (B).period 2. These findings suggest that further implementation strategies are needed to improve LCS care gap closure.Study analyses considered care gap closure as a multifactor outcome, including LDCT completion, other chest CTs, and SDM documentation.These alternative approaches to care gap closure accounted for a substantial proportion of overall closures, with 10.4% attributed to other chest CTs and 12.7% to SDM documentation, compared with 23.7% for LDCT.Notably, the National Committee for Quality Assurance announced plans to introduce a Healthcare Effectiveness Data and Information Set quality measure for LCS; however, it is unclear whether these measures would address alternative strategies to care gap closure.21Whilesuchqualitymeasuresaredefined,findings of our study suggest the need to account for LCS care gap closures through not only LDCT, but also other chest CTs and SDM.This study has several strengths.It evaluated a multifaceted, standards-based, and EHR-integrated intervention that could potentially be widely scaled.8Furthermore,thisstudyevaluatedadditiveoutcomesassociated with simple patient portal reminders using detailed smoking history in the EHR to assess eligibility; this has not been studied to date, to our knowledge.Additionally, this study found that other chest CTs and SDM combined were approximately as common (23.1%) as LDCTs (23.7%) for closing LCS care gaps in phase 2. This is consistent with prior literature identifying that most patients have lung cancer identified through chest imaging outside of LDCTs.22Thisstudyidentifiedseveral areas need for further research and improvement.As identified by us and others previously[23][24][25][26]and as underscored by the large number of patients with unknown LCS eligibility in this study, there is a need to improve the documentation of detailed smoking history in the EHR.Moreover, 8.5% of patients in this study had an LDCT ordered in the past year but did not complete it, indicating the need for improving follow-through after LDCT ordering.Even with clinician-and patient-facing interventions, LCS care gaps remained in more than half of LCS-eligible patients, and the LCS tool was used with only 13.4% of eligible patients, suggesting the need for further research to test implementation strategies to improve SDM and LCS.Additionally, as shown by the modest change in outcome associated with the simple patient portal reminder, more research may be needed on improving patient education and empowerment through more engaging patientfacing interventions.Accordingly, a new multisite trial is underway to evaluate the additive outcome

eTable 1 .
Patient Inclusion and Exclusion Criteria eTable 2. Overall Patient Population Demographics eFigure 2. Covariate Balancing Propensity Score Model and Improvement in Covariates Balance for Baseline and Intervention Periods 1 and 2